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@@ -49,12 +49,7 @@ using namespace cv::gpu;
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void cv::gpu::StereoBeliefPropagation::estimateRecommendedParams(int, int, int&, int&, int&) { throw_no_cuda(); }
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cv::gpu::StereoBeliefPropagation::StereoBeliefPropagation(int, int, int, int) { throw_no_cuda(); }
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cv::gpu::StereoBeliefPropagation::StereoBeliefPropagation(int, int, int, float, float, float, float, int) { throw_no_cuda(); }
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void cv::gpu::StereoBeliefPropagation::operator()(const GpuMat&, const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
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void cv::gpu::StereoBeliefPropagation::operator()(const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
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Ptr<gpu::StereoBeliefPropagation> cv::gpu::createStereoBeliefPropagation(int, int, int, int) { throw_no_cuda(); return Ptr<gpu::StereoBeliefPropagation>(); }
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#else /* !defined (HAVE_CUDA) */
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@@ -78,14 +73,295 @@ namespace cv { namespace gpu { namespace cudev
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}
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}}}
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using namespace ::cv::gpu::cudev::stereobp;
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namespace
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{
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class StereoBPImpl : public gpu::StereoBeliefPropagation
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{
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public:
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StereoBPImpl(int ndisp, int iters, int levels, int msg_type);
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void compute(InputArray left, InputArray right, OutputArray disparity);
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void compute(InputArray left, InputArray right, OutputArray disparity, Stream& stream);
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void compute(InputArray data, OutputArray disparity, Stream& stream);
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int getMinDisparity() const { return 0; }
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void setMinDisparity(int /*minDisparity*/) {}
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int getNumDisparities() const { return ndisp_; }
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void setNumDisparities(int numDisparities) { ndisp_ = numDisparities; }
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int getBlockSize() const { return 0; }
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void setBlockSize(int /*blockSize*/) {}
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int getSpeckleWindowSize() const { return 0; }
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void setSpeckleWindowSize(int /*speckleWindowSize*/) {}
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int getSpeckleRange() const { return 0; }
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void setSpeckleRange(int /*speckleRange*/) {}
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int getDisp12MaxDiff() const { return 0; }
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void setDisp12MaxDiff(int /*disp12MaxDiff*/) {}
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int getNumIters() const { return iters_; }
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void setNumIters(int iters) { iters_ = iters; }
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int getNumLevels() const { return levels_; }
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void setNumLevels(int levels) { levels_ = levels; }
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double getMaxDataTerm() const { return max_data_term_; }
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void setMaxDataTerm(double max_data_term) { max_data_term_ = (float) max_data_term; }
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double getDataWeight() const { return data_weight_; }
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void setDataWeight(double data_weight) { data_weight_ = (float) data_weight; }
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double getMaxDiscTerm() const { return max_disc_term_; }
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void setMaxDiscTerm(double max_disc_term) { max_disc_term_ = (float) max_disc_term; }
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double getDiscSingleJump() const { return disc_single_jump_; }
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void setDiscSingleJump(double disc_single_jump) { disc_single_jump_ = (float) disc_single_jump; }
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int getMsgType() const { return msg_type_; }
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void setMsgType(int msg_type) { msg_type_ = msg_type; }
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private:
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void init(Stream& stream);
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void calcBP(OutputArray disp, Stream& stream);
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int ndisp_;
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int iters_;
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int levels_;
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float max_data_term_;
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float data_weight_;
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float max_disc_term_;
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float disc_single_jump_;
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int msg_type_;
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float scale_;
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int rows_, cols_;
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std::vector<int> cols_all_, rows_all_;
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GpuMat u_, d_, l_, r_, u2_, d2_, l2_, r2_;
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std::vector<GpuMat> datas_;
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GpuMat outBuf_;
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};
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const float DEFAULT_MAX_DATA_TERM = 10.0f;
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const float DEFAULT_DATA_WEIGHT = 0.07f;
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const float DEFAULT_MAX_DISC_TERM = 1.7f;
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const float DEFAULT_DISC_SINGLE_JUMP = 1.0f;
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StereoBPImpl::StereoBPImpl(int ndisp, int iters, int levels, int msg_type) :
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ndisp_(ndisp), iters_(iters), levels_(levels),
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max_data_term_(DEFAULT_MAX_DATA_TERM), data_weight_(DEFAULT_DATA_WEIGHT),
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max_disc_term_(DEFAULT_MAX_DISC_TERM), disc_single_jump_(DEFAULT_DISC_SINGLE_JUMP),
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msg_type_(msg_type)
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{
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}
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void StereoBPImpl::compute(InputArray left, InputArray right, OutputArray disparity)
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{
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compute(left, right, disparity, Stream::Null());
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}
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void StereoBPImpl::compute(InputArray _left, InputArray _right, OutputArray disparity, Stream& stream)
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{
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using namespace cv::gpu::cudev::stereobp;
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typedef void (*comp_data_t)(const PtrStepSzb& left, const PtrStepSzb& right, const PtrStepSzb& data, cudaStream_t stream);
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static const comp_data_t comp_data_callers[2][5] =
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{
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{0, comp_data_gpu<unsigned char, short>, 0, comp_data_gpu<uchar3, short>, comp_data_gpu<uchar4, short>},
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{0, comp_data_gpu<unsigned char, float>, 0, comp_data_gpu<uchar3, float>, comp_data_gpu<uchar4, float>}
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};
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scale_ = msg_type_ == CV_32F ? 1.0f : 10.0f;
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CV_Assert( 0 < ndisp_ && 0 < iters_ && 0 < levels_ );
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CV_Assert( msg_type_ == CV_32F || msg_type_ == CV_16S );
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CV_Assert( msg_type_ == CV_32F || (1 << (levels_ - 1)) * scale_ * max_data_term_ < std::numeric_limits<short>::max() );
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GpuMat left = _left.getGpuMat();
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GpuMat right = _right.getGpuMat();
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CV_Assert( left.type() == CV_8UC1 || left.type() == CV_8UC3 || left.type() == CV_8UC4 );
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CV_Assert( left.size() == right.size() && left.type() == right.type() );
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rows_ = left.rows;
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cols_ = left.cols;
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const int divisor = (int) pow(2.f, levels_ - 1.0f);
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const int lowest_cols = cols_ / divisor;
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const int lowest_rows = rows_ / divisor;
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const int min_image_dim_size = 2;
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CV_Assert( std::min(lowest_cols, lowest_rows) > min_image_dim_size );
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init(stream);
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datas_[0].create(rows_ * ndisp_, cols_, msg_type_);
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comp_data_callers[msg_type_ == CV_32F][left.channels()](left, right, datas_[0], StreamAccessor::getStream(stream));
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calcBP(disparity, stream);
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}
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void StereoBPImpl::compute(InputArray _data, OutputArray disparity, Stream& stream)
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{
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scale_ = msg_type_ == CV_32F ? 1.0f : 10.0f;
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CV_Assert( 0 < ndisp_ && 0 < iters_ && 0 < levels_ );
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CV_Assert( msg_type_ == CV_32F || msg_type_ == CV_16S );
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CV_Assert( msg_type_ == CV_32F || (1 << (levels_ - 1)) * scale_ * max_data_term_ < std::numeric_limits<short>::max() );
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GpuMat data = _data.getGpuMat();
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CV_Assert( (data.type() == msg_type_) && (data.rows % ndisp_ == 0) );
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rows_ = data.rows / ndisp_;
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cols_ = data.cols;
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const int divisor = (int) pow(2.f, levels_ - 1.0f);
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const int lowest_cols = cols_ / divisor;
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const int lowest_rows = rows_ / divisor;
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const int min_image_dim_size = 2;
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CV_Assert( std::min(lowest_cols, lowest_rows) > min_image_dim_size );
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init(stream);
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data.copyTo(datas_[0], stream);
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calcBP(disparity, stream);
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}
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void StereoBPImpl::init(Stream& stream)
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{
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using namespace cv::gpu::cudev::stereobp;
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u_.create(rows_ * ndisp_, cols_, msg_type_);
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d_.create(rows_ * ndisp_, cols_, msg_type_);
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l_.create(rows_ * ndisp_, cols_, msg_type_);
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r_.create(rows_ * ndisp_, cols_, msg_type_);
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if (levels_ & 1)
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{
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//can clear less area
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u_.setTo(0, stream);
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d_.setTo(0, stream);
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l_.setTo(0, stream);
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r_.setTo(0, stream);
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}
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if (levels_ > 1)
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{
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int less_rows = (rows_ + 1) / 2;
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int less_cols = (cols_ + 1) / 2;
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u2_.create(less_rows * ndisp_, less_cols, msg_type_);
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d2_.create(less_rows * ndisp_, less_cols, msg_type_);
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l2_.create(less_rows * ndisp_, less_cols, msg_type_);
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r2_.create(less_rows * ndisp_, less_cols, msg_type_);
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if ((levels_ & 1) == 0)
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{
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u2_.setTo(0, stream);
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d2_.setTo(0, stream);
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l2_.setTo(0, stream);
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r2_.setTo(0, stream);
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}
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}
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load_constants(ndisp_, max_data_term_, scale_ * data_weight_, scale_ * max_disc_term_, scale_ * disc_single_jump_);
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datas_.resize(levels_);
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cols_all_.resize(levels_);
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rows_all_.resize(levels_);
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cols_all_[0] = cols_;
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rows_all_[0] = rows_;
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}
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void StereoBPImpl::calcBP(OutputArray disp, Stream& _stream)
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{
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using namespace cv::gpu::cudev::stereobp;
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typedef void (*data_step_down_t)(int dst_cols, int dst_rows, int src_rows, const PtrStepSzb& src, const PtrStepSzb& dst, cudaStream_t stream);
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static const data_step_down_t data_step_down_callers[2] =
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{
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data_step_down_gpu<short>, data_step_down_gpu<float>
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};
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typedef void (*level_up_messages_t)(int dst_idx, int dst_cols, int dst_rows, int src_rows, PtrStepSzb* mus, PtrStepSzb* mds, PtrStepSzb* mls, PtrStepSzb* mrs, cudaStream_t stream);
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static const level_up_messages_t level_up_messages_callers[2] =
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{
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level_up_messages_gpu<short>, level_up_messages_gpu<float>
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};
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typedef void (*calc_all_iterations_t)(int cols, int rows, int iters, const PtrStepSzb& u, const PtrStepSzb& d, const PtrStepSzb& l, const PtrStepSzb& r, const PtrStepSzb& data, cudaStream_t stream);
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static const calc_all_iterations_t calc_all_iterations_callers[2] =
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{
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calc_all_iterations_gpu<short>, calc_all_iterations_gpu<float>
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};
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typedef void (*output_t)(const PtrStepSzb& u, const PtrStepSzb& d, const PtrStepSzb& l, const PtrStepSzb& r, const PtrStepSzb& data, const PtrStepSz<short>& disp, cudaStream_t stream);
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static const output_t output_callers[2] =
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{
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output_gpu<short>, output_gpu<float>
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};
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const int funcIdx = msg_type_ == CV_32F;
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cudaStream_t stream = StreamAccessor::getStream(_stream);
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for (int i = 1; i < levels_; ++i)
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{
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cols_all_[i] = (cols_all_[i-1] + 1) / 2;
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rows_all_[i] = (rows_all_[i-1] + 1) / 2;
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datas_[i].create(rows_all_[i] * ndisp_, cols_all_[i], msg_type_);
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data_step_down_callers[funcIdx](cols_all_[i], rows_all_[i], rows_all_[i-1], datas_[i-1], datas_[i], stream);
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}
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PtrStepSzb mus[] = {u_, u2_};
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PtrStepSzb mds[] = {d_, d2_};
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PtrStepSzb mrs[] = {r_, r2_};
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PtrStepSzb mls[] = {l_, l2_};
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int mem_idx = (levels_ & 1) ? 0 : 1;
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for (int i = levels_ - 1; i >= 0; --i)
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{
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// for lower level we have already computed messages by setting to zero
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if (i != levels_ - 1)
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level_up_messages_callers[funcIdx](mem_idx, cols_all_[i], rows_all_[i], rows_all_[i+1], mus, mds, mls, mrs, stream);
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calc_all_iterations_callers[funcIdx](cols_all_[i], rows_all_[i], iters_, mus[mem_idx], mds[mem_idx], mls[mem_idx], mrs[mem_idx], datas_[i], stream);
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mem_idx = (mem_idx + 1) & 1;
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}
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const int dtype = disp.fixedType() ? disp.type() : CV_16SC1;
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disp.create(rows_, cols_, dtype);
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GpuMat out = disp.getGpuMat();
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if (dtype != CV_16SC1)
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{
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outBuf_.create(rows_, cols_, CV_16SC1);
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out = outBuf_;
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}
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out.setTo(0, _stream);
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output_callers[funcIdx](u_, d_, l_, r_, datas_.front(), out, stream);
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if (dtype != CV_16SC1)
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out.convertTo(disp, dtype, _stream);
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}
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}
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Ptr<gpu::StereoBeliefPropagation> cv::gpu::createStereoBeliefPropagation(int ndisp, int iters, int levels, int msg_type)
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{
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return new StereoBPImpl(ndisp, iters, levels, msg_type);
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}
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void cv::gpu::StereoBeliefPropagation::estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels)
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@@ -101,240 +377,4 @@ void cv::gpu::StereoBeliefPropagation::estimateRecommendedParams(int width, int
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if (levels == 0) levels++;
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}
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cv::gpu::StereoBeliefPropagation::StereoBeliefPropagation(int ndisp_, int iters_, int levels_, int msg_type_)
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: ndisp(ndisp_), iters(iters_), levels(levels_),
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max_data_term(DEFAULT_MAX_DATA_TERM), data_weight(DEFAULT_DATA_WEIGHT),
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max_disc_term(DEFAULT_MAX_DISC_TERM), disc_single_jump(DEFAULT_DISC_SINGLE_JUMP),
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msg_type(msg_type_), datas(levels_)
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{
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}
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cv::gpu::StereoBeliefPropagation::StereoBeliefPropagation(int ndisp_, int iters_, int levels_, float max_data_term_, float data_weight_, float max_disc_term_, float disc_single_jump_, int msg_type_)
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: ndisp(ndisp_), iters(iters_), levels(levels_),
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max_data_term(max_data_term_), data_weight(data_weight_),
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max_disc_term(max_disc_term_), disc_single_jump(disc_single_jump_),
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msg_type(msg_type_), datas(levels_)
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{
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}
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namespace
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{
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class StereoBeliefPropagationImpl
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{
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public:
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StereoBeliefPropagationImpl(StereoBeliefPropagation& rthis_,
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GpuMat& u_, GpuMat& d_, GpuMat& l_, GpuMat& r_,
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GpuMat& u2_, GpuMat& d2_, GpuMat& l2_, GpuMat& r2_,
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std::vector<GpuMat>& datas_, GpuMat& out_)
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: rthis(rthis_), u(u_), d(d_), l(l_), r(r_), u2(u2_), d2(d2_), l2(l2_), r2(r2_), datas(datas_), out(out_),
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zero(Scalar::all(0)), scale(rthis_.msg_type == CV_32F ? 1.0f : 10.0f)
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{
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CV_Assert(0 < rthis.ndisp && 0 < rthis.iters && 0 < rthis.levels);
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CV_Assert(rthis.msg_type == CV_32F || rthis.msg_type == CV_16S);
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CV_Assert(rthis.msg_type == CV_32F || (1 << (rthis.levels - 1)) * scale * rthis.max_data_term < std::numeric_limits<short>::max());
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}
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void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disp, Stream& stream)
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|
{
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|
typedef void (*comp_data_t)(const PtrStepSzb& left, const PtrStepSzb& right, const PtrStepSzb& data, cudaStream_t stream);
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|
static const comp_data_t comp_data_callers[2][5] =
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|
{
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|
{0, comp_data_gpu<unsigned char, short>, 0, comp_data_gpu<uchar3, short>, comp_data_gpu<uchar4, short>},
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|
{0, comp_data_gpu<unsigned char, float>, 0, comp_data_gpu<uchar3, float>, comp_data_gpu<uchar4, float>}
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|
};
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|
CV_Assert(left.size() == right.size() && left.type() == right.type());
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|
CV_Assert(left.type() == CV_8UC1 || left.type() == CV_8UC3 || left.type() == CV_8UC4);
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rows = left.rows;
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cols = left.cols;
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int divisor = (int)pow(2.f, rthis.levels - 1.0f);
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|
int lowest_cols = cols / divisor;
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|
int lowest_rows = rows / divisor;
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|
const int min_image_dim_size = 2;
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|
CV_Assert(std::min(lowest_cols, lowest_rows) > min_image_dim_size);
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|
init(stream);
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|
datas[0].create(rows * rthis.ndisp, cols, rthis.msg_type);
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|
comp_data_callers[rthis.msg_type == CV_32F][left.channels()](left, right, datas[0], StreamAccessor::getStream(stream));
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|
|
calcBP(disp, stream);
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|
|
|
}
|
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|
|
|
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|
|
|
void operator()(const GpuMat& data, GpuMat& disp, Stream& stream)
|
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|
|
|
{
|
|
|
|
|
CV_Assert((data.type() == rthis.msg_type) && (data.rows % rthis.ndisp == 0));
|
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|
|
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|
rows = data.rows / rthis.ndisp;
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|
cols = data.cols;
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|
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|
|
int divisor = (int)pow(2.f, rthis.levels - 1.0f);
|
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|
|
int lowest_cols = cols / divisor;
|
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|
|
|
int lowest_rows = rows / divisor;
|
|
|
|
|
const int min_image_dim_size = 2;
|
|
|
|
|
CV_Assert(std::min(lowest_cols, lowest_rows) > min_image_dim_size);
|
|
|
|
|
|
|
|
|
|
init(stream);
|
|
|
|
|
|
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|
|
|
datas[0] = data;
|
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|
|
|
|
|
|
|
|
calcBP(disp, stream);
|
|
|
|
|
}
|
|
|
|
|
private:
|
|
|
|
|
void init(Stream& stream)
|
|
|
|
|
{
|
|
|
|
|
u.create(rows * rthis.ndisp, cols, rthis.msg_type);
|
|
|
|
|
d.create(rows * rthis.ndisp, cols, rthis.msg_type);
|
|
|
|
|
l.create(rows * rthis.ndisp, cols, rthis.msg_type);
|
|
|
|
|
r.create(rows * rthis.ndisp, cols, rthis.msg_type);
|
|
|
|
|
|
|
|
|
|
if (rthis.levels & 1)
|
|
|
|
|
{
|
|
|
|
|
//can clear less area
|
|
|
|
|
u.setTo(zero, stream);
|
|
|
|
|
d.setTo(zero, stream);
|
|
|
|
|
l.setTo(zero, stream);
|
|
|
|
|
r.setTo(zero, stream);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
if (rthis.levels > 1)
|
|
|
|
|
{
|
|
|
|
|
int less_rows = (rows + 1) / 2;
|
|
|
|
|
int less_cols = (cols + 1) / 2;
|
|
|
|
|
|
|
|
|
|
u2.create(less_rows * rthis.ndisp, less_cols, rthis.msg_type);
|
|
|
|
|
d2.create(less_rows * rthis.ndisp, less_cols, rthis.msg_type);
|
|
|
|
|
l2.create(less_rows * rthis.ndisp, less_cols, rthis.msg_type);
|
|
|
|
|
r2.create(less_rows * rthis.ndisp, less_cols, rthis.msg_type);
|
|
|
|
|
|
|
|
|
|
if ((rthis.levels & 1) == 0)
|
|
|
|
|
{
|
|
|
|
|
u2.setTo(zero, stream);
|
|
|
|
|
d2.setTo(zero, stream);
|
|
|
|
|
l2.setTo(zero, stream);
|
|
|
|
|
r2.setTo(zero, stream);
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
load_constants(rthis.ndisp, rthis.max_data_term, scale * rthis.data_weight, scale * rthis.max_disc_term, scale * rthis.disc_single_jump);
|
|
|
|
|
|
|
|
|
|
datas.resize(rthis.levels);
|
|
|
|
|
|
|
|
|
|
cols_all.resize(rthis.levels);
|
|
|
|
|
rows_all.resize(rthis.levels);
|
|
|
|
|
|
|
|
|
|
cols_all[0] = cols;
|
|
|
|
|
rows_all[0] = rows;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
void calcBP(GpuMat& disp, Stream& stream)
|
|
|
|
|
{
|
|
|
|
|
typedef void (*data_step_down_t)(int dst_cols, int dst_rows, int src_rows, const PtrStepSzb& src, const PtrStepSzb& dst, cudaStream_t stream);
|
|
|
|
|
static const data_step_down_t data_step_down_callers[2] =
|
|
|
|
|
{
|
|
|
|
|
data_step_down_gpu<short>, data_step_down_gpu<float>
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
typedef void (*level_up_messages_t)(int dst_idx, int dst_cols, int dst_rows, int src_rows, PtrStepSzb* mus, PtrStepSzb* mds, PtrStepSzb* mls, PtrStepSzb* mrs, cudaStream_t stream);
|
|
|
|
|
static const level_up_messages_t level_up_messages_callers[2] =
|
|
|
|
|
{
|
|
|
|
|
level_up_messages_gpu<short>, level_up_messages_gpu<float>
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
typedef void (*calc_all_iterations_t)(int cols, int rows, int iters, const PtrStepSzb& u, const PtrStepSzb& d, const PtrStepSzb& l, const PtrStepSzb& r, const PtrStepSzb& data, cudaStream_t stream);
|
|
|
|
|
static const calc_all_iterations_t calc_all_iterations_callers[2] =
|
|
|
|
|
{
|
|
|
|
|
calc_all_iterations_gpu<short>, calc_all_iterations_gpu<float>
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
typedef void (*output_t)(const PtrStepSzb& u, const PtrStepSzb& d, const PtrStepSzb& l, const PtrStepSzb& r, const PtrStepSzb& data, const PtrStepSz<short>& disp, cudaStream_t stream);
|
|
|
|
|
static const output_t output_callers[2] =
|
|
|
|
|
{
|
|
|
|
|
output_gpu<short>, output_gpu<float>
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
const int funcIdx = rthis.msg_type == CV_32F;
|
|
|
|
|
|
|
|
|
|
cudaStream_t cudaStream = StreamAccessor::getStream(stream);
|
|
|
|
|
|
|
|
|
|
for (int i = 1; i < rthis.levels; ++i)
|
|
|
|
|
{
|
|
|
|
|
cols_all[i] = (cols_all[i-1] + 1) / 2;
|
|
|
|
|
rows_all[i] = (rows_all[i-1] + 1) / 2;
|
|
|
|
|
|
|
|
|
|
datas[i].create(rows_all[i] * rthis.ndisp, cols_all[i], rthis.msg_type);
|
|
|
|
|
|
|
|
|
|
data_step_down_callers[funcIdx](cols_all[i], rows_all[i], rows_all[i-1], datas[i-1], datas[i], cudaStream);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
PtrStepSzb mus[] = {u, u2};
|
|
|
|
|
PtrStepSzb mds[] = {d, d2};
|
|
|
|
|
PtrStepSzb mrs[] = {r, r2};
|
|
|
|
|
PtrStepSzb mls[] = {l, l2};
|
|
|
|
|
|
|
|
|
|
int mem_idx = (rthis.levels & 1) ? 0 : 1;
|
|
|
|
|
|
|
|
|
|
for (int i = rthis.levels - 1; i >= 0; --i)
|
|
|
|
|
{
|
|
|
|
|
// for lower level we have already computed messages by setting to zero
|
|
|
|
|
if (i != rthis.levels - 1)
|
|
|
|
|
level_up_messages_callers[funcIdx](mem_idx, cols_all[i], rows_all[i], rows_all[i+1], mus, mds, mls, mrs, cudaStream);
|
|
|
|
|
|
|
|
|
|
calc_all_iterations_callers[funcIdx](cols_all[i], rows_all[i], rthis.iters, mus[mem_idx], mds[mem_idx], mls[mem_idx], mrs[mem_idx], datas[i], cudaStream);
|
|
|
|
|
|
|
|
|
|
mem_idx = (mem_idx + 1) & 1;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
if (disp.empty())
|
|
|
|
|
disp.create(rows, cols, CV_16S);
|
|
|
|
|
|
|
|
|
|
out = ((disp.type() == CV_16S) ? disp : (out.create(rows, cols, CV_16S), out));
|
|
|
|
|
|
|
|
|
|
out.setTo(zero, stream);
|
|
|
|
|
|
|
|
|
|
output_callers[funcIdx](u, d, l, r, datas.front(), out, cudaStream);
|
|
|
|
|
|
|
|
|
|
if (disp.type() != CV_16S)
|
|
|
|
|
out.convertTo(disp, disp.type(), stream);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
StereoBeliefPropagation& rthis;
|
|
|
|
|
|
|
|
|
|
GpuMat& u;
|
|
|
|
|
GpuMat& d;
|
|
|
|
|
GpuMat& l;
|
|
|
|
|
GpuMat& r;
|
|
|
|
|
|
|
|
|
|
GpuMat& u2;
|
|
|
|
|
GpuMat& d2;
|
|
|
|
|
GpuMat& l2;
|
|
|
|
|
GpuMat& r2;
|
|
|
|
|
|
|
|
|
|
std::vector<GpuMat>& datas;
|
|
|
|
|
GpuMat& out;
|
|
|
|
|
|
|
|
|
|
const Scalar zero;
|
|
|
|
|
const float scale;
|
|
|
|
|
|
|
|
|
|
int rows, cols;
|
|
|
|
|
|
|
|
|
|
std::vector<int> cols_all, rows_all;
|
|
|
|
|
};
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
void cv::gpu::StereoBeliefPropagation::operator()(const GpuMat& left, const GpuMat& right, GpuMat& disp, Stream& stream)
|
|
|
|
|
{
|
|
|
|
|
StereoBeliefPropagationImpl impl(*this, u, d, l, r, u2, d2, l2, r2, datas, out);
|
|
|
|
|
impl(left, right, disp, stream);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
void cv::gpu::StereoBeliefPropagation::operator()(const GpuMat& data, GpuMat& disp, Stream& stream)
|
|
|
|
|
{
|
|
|
|
|
StereoBeliefPropagationImpl impl(*this, u, d, l, r, u2, d2, l2, r2, datas, out);
|
|
|
|
|
impl(data, disp, stream);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
#endif /* !defined (HAVE_CUDA) */
|
|
|
|
|